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JCSE, vol. 6, no. 2, pp.143-150, June, 2012

DOI: http://dx.doi.org/10.5626/JCSE.2012.6.2.143

Topic Classification for Suicidology

Jonathon Read, Erik Velldal, Lilja Øvrelid
Language Technology Group, Department of Informatics, University of Oslo, Norway

Abstract: Computational techniques for topic classification can support qualitative research by automatically applying labels in preparation for qualitative analyses. This paper presents an evaluation of supervised learning techniques applied to one such use case, namely, that of labeling emotions, instructions and information in suicide notes. We train a collection of one-versus-all binary support vector machine classifiers, using cost-sensitive learning to deal with class imbalance. The features investigated range from a simple bag-of-words and n-grams over stems, to information drawn from syntactic dependency analysis and WordNet synonym sets. The experimental results are complemented by an analysis of systematic errors in both the output of our system and the gold-standard annotations.

Keyword: Affect recognition; Sentiment analysis; Skewed class distribution; Text classification

Full Paper:   149 Downloads, 4238 View

 
 
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